Defesa de Dissertação de Mestrado: Comparative Study of Transformers in Spatiotemporal Precipitation Forecasting

Orientadores:
Fabio André Machado Porto - Laboratório Nacional de Computação Científica - LNCC

Banca Examinadora:
Fabio André Machado Porto - Laboratório Nacional de Computação Científica - LNCC (presidente)
Gilson Antônio Giraldi - Laboratório Nacional de Computação Científica - LNCC
José Antônio de Fernandes Macedo - UFC
Eduardo Bezerra da Silva - CEFET - RJ

Suplentes:
Pablo Javier Blanco - Laboratório Nacional de Computação Científica - LNCC

Resumo:

Precipitation is a meteorological phenomenon that plays a fundamental role in numerous human activities, which creates the need to develop models to predict it. Deep Learning Models (DLMs) have excelled in various time series prediction tas ks. Among these DLMs, it is worth highlighting the Transformer based models, which have stood out with their attention mechanisms. This study aims to investigate the applicability of transformer models, compared to classical models for spatiotemporal precipitation prediction, highlighting how attention mechanisms detect spatiotemporal patterns.